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About 10,000 papers for “Spatial variability”

Spatial variability

Sung-geun Song․seung-tae Chung․sung-gu Kang․sang-hun Lee · 2000

This research is to develop satiable battery charger with a variety of capacity and voltage specifications of battery. For this, voltage or current were controlled through buck converter which is DC voltage that already received three-phase at primary side and passed bridge rectifier diode. And, it was comprised of full-bridge converter and HFTR for insulation and a square wave AC. The transformer primary side was comprised in series to divide certain charging current and the secondly side was comprised of 6 fixed transformers so that they can generate certain amount of power and various output voltage through relay parallel compound 6 DC Link outputs. To confirm such structure's verification and validity, simulation with PSIM was conducted, and validity of proposed

Cited by 0

Soil Spatial Variability

Eshel Bresler · Agronomy monograph/Agronomy · 1991

This chapter provides an introduction to the stochastic modeling approach of flow and transport in spatially variable soils, and to the concepts that are important to modelers. It emphasizes that spatial variability consideration may allow simpler models to be used. A rational modeling of field variability is the stochastic approach. The stochastic models are employed to predict the statistical moments of the variables such as crop yield, concentration, water content, and their dependence on the moments of soil properties. Hence, stochastic modeling, in which soil properties are regarded as random variables characterized by their probability density function rather than by deterministic values, is a rational framework to deal with variability and uncertainty. In this stochastic modeling approach, the statistical structure of concentration and water content as a function of space and time is determined for given, in a statistical sense, soil properties, and initial and boundary conditions.

Cited by 4

Effect of spatial variability.

Tianyue Wu (7040177), Zhaohua Sun (5617976), Wanxia Tan (12413377) et al. · Figshare · 2022

<p>(a). Maximum deviation of column position 0 mm. (b). Maximum deviation of column position 375 mm. (c). Maximum deviation of column position 525 mm.</p>

Cited by 0Open access

Spatial variability Terrain analysis

Data Reduction · 2015

ca pro s to spatial resolution using auxiliary data in a semiarid ecosystem in southeastern of soil ls because they intro-n of sample locations, ribe the reality of soil tochastic soil forming

Cited by 0

Electromethanogenic biocathode spatial variability

Ragab, Alaa I., Katuri, Krishna, Ali, Muhammad et al. · King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) · 2019

Understanding microbial community assembly helps in determining the main drivers shaping a community in a particular environment, in this case the niche environment at the cathode of an electromethanogenic reactor. Triplicate methanogenic biocathodes were enriched in microbial electrolysis cells (MEC) for 5 months, then transferred to triplicate dual-chambered microbial electrosynthesis (MES) reactors, operated at –1.0 V vs. Ag/AgCl for six batches. At the end of the experiment, triplicate samples were taken at different positions (top, center, bottom) from each biocathode for a total of 9 samples for total biomass protein analysis and 16S rRNA gene amplicon sequencing. Microbial community analyses showed that overall the biocathodes were enriched with methanogens, especially the hydrogenotrophic family Methanobacteriaceae, Methanobacterium sp., the acetoclastic Methanosarcina sp., and the methylotrophic Methanomassillicoccus sp., with an overall core community representing >97% of sequence reads in all samples. There was statistically no significant spatial variability (p > .05) observed in the distribution of these communities within and between the reactors. These results suggest deterministic community assembly and indicate the reproducibility of electromethanogenic biocathodic communities, with implications for larger-scale reactors.

Cited by 0

FLOW REGIMES SPATIAL VARIABILITY

HORVÁTH Csaba, St. BUIMAGA-IARINCA, G. ROŞIAN et al. · SHILAP Revista de lepidopterología · 2011

Flow regimes spatial variability. This study investigates the spatial distribution of annual flow regimes within the Tur River basin, using 29-year records (1979-2007) of mean monthly flow, for 7 gauging stations. A multivariate, statistical classification of regime “shape” and “magnitude” is used to group annual regimes. The classification revealed two “shape” classes with different timing of major flow peaks and three “magnitude” classes. These results suggest regime “shape” is controlled by the seasonal distribution and nature of hydroclimatic inputs. As such, a framework can be provided around which to test hypotheses, making regional classification an important tool for hydrological research, particularly over large and complex geographical domains.

Cited by 0Open access

Avalanche formation Spatial variability

Sascha Bellaire, Jürg Schweizer, Snow Stability · 2008

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:

Cited by 0

Junge Spatial Variability Relation

W. A. Petka, J. L. Harden, K. P. Mcgrath et al. · 1998

into which 943 ml of tris(hydroxymethyl)aminometh-ane (tris) (buffering range of 6.9 to 7.1) and 57 ml of SPS solution were added. The sample was mixed thoroughly before 1 ml volumes of KOH (8 M) were added incre-mentally to change the pH. The gel containing the SPS beads was left for approximately 3 days to equilibrate, at which time additional KOH was added if the desired pH was not found. The final volume of the gel contain-ing SPS beads was adjusted to 1 ml once the pH of the gel stabilized. The final concentration of the gel was 5% w/v. Concentration studies were made with samples prepared by diluting this volume.

Cited by 0

Spatial image variability analysis

Marios S. Pattichis, T. Cacoullos, Peter Solíz · 2003

In our analysis, each image is partitioned into a number of non-overlapping, spatial regions. Each spatial region is viewed as a Region of Interest (ROI). A grade is assigned to each ROI from a set of independent raters. The model is applied in lung image analysis, where a grade of 0/1 is assigned to each ROI. Here, I represents success, while 0 represents failure to detect a hypothesized pattern in the region. Methods for establishing spatial symmetry and spatial growth analysis are presented. In addition, a Bayes and a novel summation classifier are used for classifying the entire lung based on their regional grades. When compared against the readers, the two classifiers have a total misclassification error of the order of inter-rater variability error.

Cited by 0